142 papers
Systems biology moves beyond studying individual genes or proteins to understand how they work together as a complex, living network. Instead of looking at isolated parts, this field examines the intricate conversations between molecules that drive life, revealing how cellular systems respond to changes and maintain balance. It is a holistic approach that turns vast amounts of data into a coherent story of how organisms function as a whole.
At Gist.Science, we ensure these breakthroughs remain accessible to everyone by processing every new preprint in this category directly from bioRxiv. Our team generates both plain-language explanations for the curious mind and detailed technical summaries for researchers, bridging the gap between rapid scientific discovery and clear understanding.
Below are the latest preprints in systems biology, freshly curated and summarized to help you navigate the cutting edge of network science.
The paper introduces NetDes, a computational framework that infers core transcription factor regulatory networks and constructs ODE-based dynamical models from single-cell gene expression trajectories to mechanistically explain multi-step cell state transitions across various biological processes.
This study demonstrates that a commercial inactivated North American Eastern Equine Encephalitis virus vaccine protects mice against lethal Madariaga virus challenge and reduces viral dissemination despite inducing negligible neutralizing antibody titers, suggesting that non-neutralizing immune mechanisms contribute to heterologous protection.
This study demonstrates that transplanted hearts rapidly adopt the biological age of their recipients rather than retaining their donor age, driven by the systemic host environment, which suggests new strategies for expanding the pool of viable transplantable organs.
Using a novel deep-learning framework called Ciclopes, this study reveals that while mRNA degradation timing remains evolutionarily conserved across mouse and human pluripotent systems, transcriptional control of the cell cycle diverges significantly, with mouse cells maintaining elevated baseline expression and human cells relying on larger oscillatory amplitudes that further adapt during differentiation.
This paper proposes a thermodynamically consistent model demonstrating that the ultrasensitive response in bacterial replication initiation emerges from distinct, growth-condition-dependent mechanisms involving DnaA dynamics: a balance of sequestration and origin binding in slow growth, versus rapid activation-inactivation transitions with cooperative binding in fast growth.
This paper proposes a theoretical framework using an agent-based model to demonstrate that gut microbiome heterogeneity in conditions like autism arises from "behavioural degeneracy," where diverse sensory traits and learning strategies converge on similar dietary patterns and microbial states, thereby challenging the assumption that microbiome differences uniquely reflect intrinsic neurobiological causes.
This study demonstrates that varying protein availability and nutrient uptake constraints in enzyme-constrained metabolic models reveals that protein-nutrient co-limitation significantly reshapes yeast genetic interactions, causing frequent sign inversions and revealing protein-priced epistasis that standard models miss.
This study presents a reproducible, network-based decision support framework that integrates Orphadata and DisGeNET via a rigorous gene-harmonization pipeline and employs a Graph Attention Network to learn biologically informed embeddings, achieving a 400-fold improvement over random baselines in prioritizing existing drug candidates for rare diseases.
This paper proposes that "selectively advantageous instability" (SAI) is an evolutionarily favored mechanism where organisms actively destabilize specific biological information to access superior adaptive states, creating a "stabilization-destabilization complementarity" that explains the trade-offs underlying sex-specific aging, antagonistic pleiotropy, and the accumulation of irreversible damage despite the benefits of information maintenance.
The paper introduces INFORME, a framework that integrates Bayesian information-theoretic experimental design with nonlinear mixed-effects modeling to adaptively schedule patient measurements, thereby significantly reducing the number of required scans and accelerating individualized treatment predictions compared to fixed protocols.